Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations705
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory303.4 KiB
Average record size in memory440.7 B

Variable types

Numeric7
Categorical4
Text1
Boolean1

Alerts

Academic_Level is highly overall correlated with Age and 1 other fieldsHigh correlation
Addicted_Score is highly overall correlated with Affects_Academic_Performance and 4 other fieldsHigh correlation
Affects_Academic_Performance is highly overall correlated with Addicted_Score and 5 other fieldsHigh correlation
Age is highly overall correlated with Academic_Level and 1 other fieldsHigh correlation
Avg_Daily_Usage_Hours is highly overall correlated with Addicted_Score and 4 other fieldsHigh correlation
Conflicts_Over_Social_Media is highly overall correlated with Addicted_Score and 4 other fieldsHigh correlation
Gender is highly overall correlated with Academic_Level and 1 other fieldsHigh correlation
Mental_Health_Score is highly overall correlated with Addicted_Score and 4 other fieldsHigh correlation
Most_Used_Platform is highly overall correlated with Affects_Academic_PerformanceHigh correlation
Sleep_Hours_Per_Night is highly overall correlated with Addicted_Score and 4 other fieldsHigh correlation
Student_ID is uniformly distributed Uniform
Student_ID has unique values Unique

Reproduction

Analysis started2025-06-17 15:11:22.700568
Analysis finished2025-06-17 15:11:34.603200
Duration11.9 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Student_ID
Real number (ℝ)

Uniform  Unique 

Distinct705
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353
Minimum1
Maximum705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-06-17T16:11:34.793349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36.2
Q1177
median353
Q3529
95-th percentile669.8
Maximum705
Range704
Interquartile range (IQR)352

Descriptive statistics

Standard deviation203.66026
Coefficient of variation (CV)0.57694124
Kurtosis-1.2
Mean353
Median Absolute Deviation (MAD)176
Skewness0
Sum248865
Variance41477.5
MonotonicityStrictly increasing
2025-06-17T16:11:35.091059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
705 1
 
0.1%
689 1
 
0.1%
688 1
 
0.1%
687 1
 
0.1%
686 1
 
0.1%
685 1
 
0.1%
684 1
 
0.1%
683 1
 
0.1%
682 1
 
0.1%
681 1
 
0.1%
Other values (695) 695
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
705 1
0.1%
704 1
0.1%
703 1
0.1%
702 1
0.1%
701 1
0.1%
700 1
0.1%
699 1
0.1%
698 1
0.1%
697 1
0.1%
696 1
0.1%

Age
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.659574
Minimum18
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-06-17T16:11:35.380568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q119
median21
Q322
95-th percentile23
Maximum24
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.3992175
Coefficient of variation (CV)0.067727312
Kurtosis-0.50784411
Mean20.659574
Median Absolute Deviation (MAD)1
Skewness0.36890899
Sum14565
Variance1.9578095
MonotonicityNot monotonic
2025-06-17T16:11:35.609215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20 165
23.4%
19 163
23.1%
21 156
22.1%
22 147
20.9%
23 34
 
4.8%
24 26
 
3.7%
18 14
 
2.0%
ValueCountFrequency (%)
18 14
 
2.0%
19 163
23.1%
20 165
23.4%
21 156
22.1%
22 147
20.9%
23 34
 
4.8%
24 26
 
3.7%
ValueCountFrequency (%)
24 26
 
3.7%
23 34
 
4.8%
22 147
20.9%
21 156
22.1%
20 165
23.4%
19 163
23.1%
18 14
 
2.0%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.8 KiB
Female
353 
Male
352 

Length

Max length6
Median length6
Mean length5.0014184
Min length4

Characters and Unicode

Total characters3526
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 353
50.1%
Male 352
49.9%

Length

2025-06-17T16:11:35.893528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-17T16:11:36.132976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
female 353
50.1%
male 352
49.9%

Most occurring characters

ValueCountFrequency (%)
e 1058
30.0%
a 705
20.0%
l 705
20.0%
F 353
 
10.0%
m 353
 
10.0%
M 352
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3526
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1058
30.0%
a 705
20.0%
l 705
20.0%
F 353
 
10.0%
m 353
 
10.0%
M 352
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3526
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1058
30.0%
a 705
20.0%
l 705
20.0%
F 353
 
10.0%
m 353
 
10.0%
M 352
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3526
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1058
30.0%
a 705
20.0%
l 705
20.0%
F 353
 
10.0%
m 353
 
10.0%
M 352
 
10.0%

Academic_Level
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size46.7 KiB
Undergraduate
353 
Graduate
325 
High School
 
27

Length

Max length13
Median length13
Mean length10.61844
Min length8

Characters and Unicode

Total characters7486
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUndergraduate
2nd rowGraduate
3rd rowUndergraduate
4th rowHigh School
5th rowGraduate

Common Values

ValueCountFrequency (%)
Undergraduate 353
50.1%
Graduate 325
46.1%
High School 27
 
3.8%

Length

2025-06-17T16:11:36.369420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-17T16:11:36.602600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
undergraduate 353
48.2%
graduate 325
44.4%
high 27
 
3.7%
school 27
 
3.7%

Most occurring characters

ValueCountFrequency (%)
a 1356
18.1%
d 1031
13.8%
r 1031
13.8%
e 1031
13.8%
t 678
9.1%
u 678
9.1%
g 380
 
5.1%
U 353
 
4.7%
n 353
 
4.7%
G 325
 
4.3%
Other values (8) 270
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1356
18.1%
d 1031
13.8%
r 1031
13.8%
e 1031
13.8%
t 678
9.1%
u 678
9.1%
g 380
 
5.1%
U 353
 
4.7%
n 353
 
4.7%
G 325
 
4.3%
Other values (8) 270
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1356
18.1%
d 1031
13.8%
r 1031
13.8%
e 1031
13.8%
t 678
9.1%
u 678
9.1%
g 380
 
5.1%
U 353
 
4.7%
n 353
 
4.7%
G 325
 
4.3%
Other values (8) 270
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1356
18.1%
d 1031
13.8%
r 1031
13.8%
e 1031
13.8%
t 678
9.1%
u 678
9.1%
g 380
 
5.1%
U 353
 
4.7%
n 353
 
4.7%
G 325
 
4.3%
Other values (8) 270
 
3.6%
Distinct110
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
2025-06-17T16:11:36.874875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length13
Mean length6.5460993
Min length2

Characters and Unicode

Total characters4615
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)11.2%

Sample

1st rowBangladesh
2nd rowIndia
3rd rowUSA
4th rowUK
5th rowCanada
ValueCountFrequency (%)
india 53
 
7.0%
usa 40
 
5.3%
canada 34
 
4.5%
france 27
 
3.6%
mexico 27
 
3.6%
spain 27
 
3.6%
turkey 27
 
3.6%
ireland 27
 
3.6%
denmark 27
 
3.6%
switzerland 27
 
3.6%
Other values (109) 436
58.0%
2025-06-17T16:11:37.442302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 780
16.9%
n 437
 
9.5%
e 326
 
7.1%
i 325
 
7.0%
r 255
 
5.5%
d 231
 
5.0%
l 221
 
4.8%
s 147
 
3.2%
S 141
 
3.1%
t 128
 
2.8%
Other values (41) 1624
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 780
16.9%
n 437
 
9.5%
e 326
 
7.1%
i 325
 
7.0%
r 255
 
5.5%
d 231
 
5.0%
l 221
 
4.8%
s 147
 
3.2%
S 141
 
3.1%
t 128
 
2.8%
Other values (41) 1624
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 780
16.9%
n 437
 
9.5%
e 326
 
7.1%
i 325
 
7.0%
r 255
 
5.5%
d 231
 
5.0%
l 221
 
4.8%
s 147
 
3.2%
S 141
 
3.1%
t 128
 
2.8%
Other values (41) 1624
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 780
16.9%
n 437
 
9.5%
e 326
 
7.1%
i 325
 
7.0%
r 255
 
5.5%
d 231
 
5.0%
l 221
 
4.8%
s 147
 
3.2%
S 141
 
3.1%
t 128
 
2.8%
Other values (41) 1624
35.2%

Avg_Daily_Usage_Hours
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9187234
Minimum1.5
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-06-17T16:11:37.685566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.82
Q14.1
median4.8
Q35.8
95-th percentile7
Maximum8.5
Range7
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.2573945
Coefficient of variation (CV)0.25563432
Kurtosis-0.35255351
Mean4.9187234
Median Absolute Deviation (MAD)0.9
Skewness0.16463376
Sum3467.7
Variance1.581041
MonotonicityNot monotonic
2025-06-17T16:11:38.022091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.7 31
 
4.4%
4.5 30
 
4.3%
4.8 29
 
4.1%
4.6 28
 
4.0%
4.4 27
 
3.8%
4.3 23
 
3.3%
4.2 23
 
3.3%
4.9 20
 
2.8%
5.7 19
 
2.7%
3.8 19
 
2.7%
Other values (57) 456
64.7%
ValueCountFrequency (%)
1.5 1
 
0.1%
2 1
 
0.1%
2.1 1
 
0.1%
2.2 2
 
0.3%
2.3 4
0.6%
2.4 4
0.6%
2.5 5
0.7%
2.6 5
0.7%
2.7 5
0.7%
2.8 8
1.1%
ValueCountFrequency (%)
8.5 1
0.1%
8.4 1
0.1%
8.3 1
0.1%
8.2 1
0.1%
8.1 1
0.1%
8 1
0.1%
7.9 1
0.1%
7.8 1
0.1%
7.7 1
0.1%
7.6 1
0.1%

Most_Used_Platform
Categorical

High correlation 

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size44.7 KiB
Instagram
249 
TikTok
154 
Facebook
123 
WhatsApp
54 
Twitter
30 
Other values (7)
95 

Length

Max length9
Median length8
Mean length7.7829787
Min length4

Characters and Unicode

Total characters5487
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInstagram
2nd rowTwitter
3rd rowTikTok
4th rowYouTube
5th rowFacebook

Common Values

ValueCountFrequency (%)
Instagram 249
35.3%
TikTok 154
21.8%
Facebook 123
17.4%
WhatsApp 54
 
7.7%
Twitter 30
 
4.3%
LinkedIn 21
 
3.0%
WeChat 15
 
2.1%
Snapchat 13
 
1.8%
VKontakte 12
 
1.7%
LINE 12
 
1.7%
Other values (2) 22
 
3.1%

Length

2025-06-17T16:11:38.309729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
instagram 249
35.3%
tiktok 154
21.8%
facebook 123
17.4%
whatsapp 54
 
7.7%
twitter 30
 
4.3%
linkedin 21
 
3.0%
wechat 15
 
2.1%
snapchat 13
 
1.8%
vkontakte 12
 
1.7%
line 12
 
1.7%
Other values (2) 22
 
3.1%

Most occurring characters

ValueCountFrequency (%)
a 764
13.9%
k 488
 
8.9%
o 434
 
7.9%
t 415
 
7.6%
T 360
 
6.6%
n 316
 
5.8%
s 303
 
5.5%
I 282
 
5.1%
r 279
 
5.1%
g 249
 
4.5%
Other values (22) 1597
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5487
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 764
13.9%
k 488
 
8.9%
o 434
 
7.9%
t 415
 
7.6%
T 360
 
6.6%
n 316
 
5.8%
s 303
 
5.5%
I 282
 
5.1%
r 279
 
5.1%
g 249
 
4.5%
Other values (22) 1597
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5487
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 764
13.9%
k 488
 
8.9%
o 434
 
7.9%
t 415
 
7.6%
T 360
 
6.6%
n 316
 
5.8%
s 303
 
5.5%
I 282
 
5.1%
r 279
 
5.1%
g 249
 
4.5%
Other values (22) 1597
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5487
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 764
13.9%
k 488
 
8.9%
o 434
 
7.9%
t 415
 
7.6%
T 360
 
6.6%
n 316
 
5.8%
s 303
 
5.5%
I 282
 
5.1%
r 279
 
5.1%
g 249
 
4.5%
Other values (22) 1597
29.1%

Affects_Academic_Performance
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size833.0 B
True
453 
False
252 
ValueCountFrequency (%)
True 453
64.3%
False 252
35.7%
2025-06-17T16:11:38.532091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Sleep_Hours_Per_Night
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8689362
Minimum3.8
Maximum9.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-06-17T16:11:38.784714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.02
Q16
median6.9
Q37.7
95-th percentile8.6
Maximum9.6
Range5.8
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.126848
Coefficient of variation (CV)0.16404986
Kurtosis-0.51981053
Mean6.8689362
Median Absolute Deviation (MAD)0.9
Skewness-0.10904033
Sum4842.6
Variance1.2697865
MonotonicityNot monotonic
2025-06-17T16:11:39.270436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 28
 
4.0%
7.3 28
 
4.0%
7.4 26
 
3.7%
7.5 25
 
3.5%
6.7 25
 
3.5%
6.8 24
 
3.4%
6.5 23
 
3.3%
5.8 23
 
3.3%
5.9 21
 
3.0%
7.8 20
 
2.8%
Other values (49) 462
65.5%
ValueCountFrequency (%)
3.8 1
 
0.1%
3.9 1
 
0.1%
4 1
 
0.1%
4.1 2
0.3%
4.2 2
0.3%
4.3 2
0.3%
4.4 2
0.3%
4.5 3
0.4%
4.6 3
0.4%
4.7 3
0.4%
ValueCountFrequency (%)
9.6 1
 
0.1%
9.5 2
 
0.3%
9.4 2
 
0.3%
9.3 2
 
0.3%
9.2 3
0.4%
9.1 3
0.4%
9 3
0.4%
8.9 5
0.7%
8.8 6
0.9%
8.7 7
1.0%

Mental_Health_Score
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2269504
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-06-17T16:11:39.538121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q15
median6
Q37
95-th percentile8
Maximum9
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1050555
Coefficient of variation (CV)0.17746335
Kurtosis-0.83557411
Mean6.2269504
Median Absolute Deviation (MAD)1
Skewness0.049022623
Sum4390
Variance1.2211476
MonotonicityNot monotonic
2025-06-17T16:11:39.755062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 219
31.1%
7 178
25.2%
5 173
24.5%
8 105
14.9%
4 29
 
4.1%
9 1
 
0.1%
ValueCountFrequency (%)
4 29
 
4.1%
5 173
24.5%
6 219
31.1%
7 178
25.2%
8 105
14.9%
9 1
 
0.1%
ValueCountFrequency (%)
9 1
 
0.1%
8 105
14.9%
7 178
25.2%
6 219
31.1%
5 173
24.5%
4 29
 
4.1%
Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
Single
384 
In Relationship
289 
Complicated
 
32

Length

Max length15
Median length6
Mean length9.9163121
Min length6

Characters and Unicode

Total characters6991
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIn Relationship
2nd rowSingle
3rd rowComplicated
4th rowSingle
5th rowIn Relationship

Common Values

ValueCountFrequency (%)
Single 384
54.5%
In Relationship 289
41.0%
Complicated 32
 
4.5%

Length

2025-06-17T16:11:39.983216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-17T16:11:40.189503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
single 384
38.6%
in 289
29.1%
relationship 289
29.1%
complicated 32
 
3.2%

Most occurring characters

ValueCountFrequency (%)
i 994
14.2%
n 962
13.8%
l 705
10.1%
e 705
10.1%
S 384
 
5.5%
g 384
 
5.5%
t 321
 
4.6%
a 321
 
4.6%
o 321
 
4.6%
p 321
 
4.6%
Other values (9) 1573
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 994
14.2%
n 962
13.8%
l 705
10.1%
e 705
10.1%
S 384
 
5.5%
g 384
 
5.5%
t 321
 
4.6%
a 321
 
4.6%
o 321
 
4.6%
p 321
 
4.6%
Other values (9) 1573
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 994
14.2%
n 962
13.8%
l 705
10.1%
e 705
10.1%
S 384
 
5.5%
g 384
 
5.5%
t 321
 
4.6%
a 321
 
4.6%
o 321
 
4.6%
p 321
 
4.6%
Other values (9) 1573
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 994
14.2%
n 962
13.8%
l 705
10.1%
e 705
10.1%
S 384
 
5.5%
g 384
 
5.5%
t 321
 
4.6%
a 321
 
4.6%
o 321
 
4.6%
p 321
 
4.6%
Other values (9) 1573
22.5%

Conflicts_Over_Social_Media
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8496454
Minimum0
Maximum5
Zeros4
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-06-17T16:11:40.401175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.95796779
Coefficient of variation (CV)0.33617088
Kurtosis-0.38337441
Mean2.8496454
Median Absolute Deviation (MAD)1
Skewness-0.16233999
Sum2009
Variance0.91770229
MonotonicityNot monotonic
2025-06-17T16:11:40.617043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 261
37.0%
2 204
28.9%
4 174
24.7%
1 47
 
6.7%
5 15
 
2.1%
0 4
 
0.6%
ValueCountFrequency (%)
0 4
 
0.6%
1 47
 
6.7%
2 204
28.9%
3 261
37.0%
4 174
24.7%
5 15
 
2.1%
ValueCountFrequency (%)
5 15
 
2.1%
4 174
24.7%
3 261
37.0%
2 204
28.9%
1 47
 
6.7%
0 4
 
0.6%

Addicted_Score
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4368794
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-06-17T16:11:40.814220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median7
Q38
95-th percentile9
Maximum9
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5871649
Coefficient of variation (CV)0.24657366
Kurtosis-0.8944829
Mean6.4368794
Median Absolute Deviation (MAD)1
Skewness-0.29682849
Sum4538
Variance2.5190925
MonotonicityNot monotonic
2025-06-17T16:11:41.032586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 209
29.6%
8 144
20.4%
5 136
19.3%
4 83
 
11.8%
6 61
 
8.7%
9 55
 
7.8%
3 16
 
2.3%
2 1
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 16
 
2.3%
4 83
 
11.8%
5 136
19.3%
6 61
 
8.7%
7 209
29.6%
8 144
20.4%
9 55
 
7.8%
ValueCountFrequency (%)
9 55
 
7.8%
8 144
20.4%
7 209
29.6%
6 61
 
8.7%
5 136
19.3%
4 83
 
11.8%
3 16
 
2.3%
2 1
 
0.1%

Interactions

2025-06-17T16:11:32.398744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:23.542343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:25.015331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:26.427618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:27.969066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:29.397854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:30.859671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:32.610544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:23.774908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:25.219334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:26.642529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:28.170108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:29.630537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:31.073732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:32.815650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:23.975054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:25.414573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:26.853192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:28.386828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:29.837672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:31.276086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:33.018165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:24.186839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:25.642289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:27.162026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:28.579076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:30.041995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:31.477810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:33.214709image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:24.402485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:25.836735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:27.357168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:28.766565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:30.261392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:31.668101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:33.415920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:24.600329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:26.027779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:27.557451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:28.970773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:30.463564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:31.862732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:33.609569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:24.802616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:26.229499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:27.754611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:29.166901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:30.656716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-06-17T16:11:32.049942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-06-17T16:11:41.225167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Academic_LevelAddicted_ScoreAffects_Academic_PerformanceAgeAvg_Daily_Usage_HoursConflicts_Over_Social_MediaGenderMental_Health_ScoreMost_Used_PlatformRelationship_StatusSleep_Hours_Per_NightStudent_ID
Academic_Level1.0000.2460.1120.8150.2030.2290.6590.2180.3720.2240.3970.277
Addicted_Score0.2461.0000.958-0.1700.8380.9510.225-0.9500.4550.209-0.7880.032
Affects_Academic_Performance0.1120.9581.0000.1500.6860.9880.0000.9120.5950.1720.6390.036
Age0.815-0.1700.1501.000-0.102-0.1780.6780.1580.3210.1330.1320.194
Avg_Daily_Usage_Hours0.2030.8380.686-0.1021.0000.8030.128-0.8020.2860.177-0.8140.235
Conflicts_Over_Social_Media0.2290.9510.988-0.1780.8031.0000.128-0.9080.4310.211-0.7230.128
Gender0.6590.2250.0000.6780.1280.1281.0000.1050.4510.0000.1470.000
Mental_Health_Score0.218-0.9500.9120.158-0.802-0.9080.1051.0000.3410.1610.734-0.041
Most_Used_Platform0.3720.4550.5950.3210.2860.4310.4510.3411.0000.2810.2000.285
Relationship_Status0.2240.2090.1720.1330.1770.2110.0000.1610.2811.0000.1980.303
Sleep_Hours_Per_Night0.397-0.7880.6390.132-0.814-0.7230.1470.7340.2000.1981.0000.170
Student_ID0.2770.0320.0360.1940.2350.1280.000-0.0410.2850.3030.1701.000

Missing values

2025-06-17T16:11:33.950880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-17T16:11:34.369226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Student_IDAgeGenderAcademic_LevelCountryAvg_Daily_Usage_HoursMost_Used_PlatformAffects_Academic_PerformanceSleep_Hours_Per_NightMental_Health_ScoreRelationship_StatusConflicts_Over_Social_MediaAddicted_Score
0119FemaleUndergraduateBangladesh5.2InstagramYes6.56In Relationship38
1222MaleGraduateIndia2.1TwitterNo7.58Single03
2320FemaleUndergraduateUSA6.0TikTokYes5.05Complicated49
3418MaleHigh SchoolUK3.0YouTubeNo7.07Single14
4521MaleGraduateCanada4.5FacebookYes6.06In Relationship27
5619FemaleUndergraduateAustralia7.2InstagramYes4.54Complicated59
6723MaleGraduateGermany1.5LinkedInNo8.09Single02
7820FemaleUndergraduateBrazil5.8SnapchatYes6.06In Relationship28
8918MaleHigh SchoolJapan4.0TikTokNo6.57Single15
91021FemaleGraduateSouth Korea3.3InstagramNo7.07In Relationship14
Student_IDAgeGenderAcademic_LevelCountryAvg_Daily_Usage_HoursMost_Used_PlatformAffects_Academic_PerformanceSleep_Hours_Per_NightMental_Health_ScoreRelationship_StatusConflicts_Over_Social_MediaAddicted_Score
69569623MaleGraduateUSA5.5TwitterYes6.76In Relationship37
69669721FemaleUndergraduateMexico6.3TikTokYes6.25Single48
69769824MaleGraduateFrance4.8FacebookNo7.17In Relationship25
69869919FemaleUndergraduateCanada5.7InstagramYes6.66Single37
69970022MaleGraduateUK6.2TwitterYes6.35Single48
70070120FemaleUndergraduateItaly4.7TikTokNo7.27In Relationship25
70170223MaleGraduateRussia6.8InstagramYes5.94Single59
70270321FemaleUndergraduateChina5.6WeChatYes6.76In Relationship37
70370424MaleGraduateJapan4.3TwitterNo7.58Single24
70470519FemaleUndergraduatePoland6.2FacebookYes6.35Single48